arXiv:2607. 10487v1 Announce Type: cross Abstract: LLM agents can commit durable effects from authority evidence that was valid earlier in execution: a DOM snapshot, approval epoch, version witness, branch token, or worker result.
arXiv:2606. 22504v1 Announce Type: cross Abstract: Coding agents often receive broad tool access for an entire task, even when a resource is needed only for one subgoal.
The paper investigates where exactly‑once semantics should be enforced for tool‑using agents—within the model, the agent harness, or the tool contract—by evaluating 25,930 episodes across nine models, three harnesses, two contract variants, and fifteen recovery conditions. Using the LIMBO sandbox, the study shows that when an immediate read‑back is available, frontier models rarely duplicate lost acknowledgements, whereas weaker models do; when read‑back is unavailable, the contract’s idempotency keys explain most duplicate behavior. The authors prove that verification‑only policies cannot guarantee exactly‑once under late commits without bounded in‑flight time, and that waiting only helps when delays are short and predictable. whyItMatters":"The findings clarify that enforcing exactly‑once semantics largely depends on the tool contract and fault type, guiding designers on where to focus reliability mechanisms for LLM agents."
arXiv:2609. 00546v1 Announce Type: cross Abstract: Agent systems are commonly described by the model and harness that currently produce their behavior.
arXiv:2608.24569v2 Announce Type: replace Abstract: Large language model (LLM) agents build on one another's work through summaries, plans, and other intermediate artifacts. These records let a downs...
arXiv:2609.21284v1 Announce Type: cross Abstract: Long-running AI agents outlive initiating processes through credentials, delegated tasks, queues, callbacks, reservations, and provider-side operatio...
The paper reports a failure study of a production agentic software‑delivery platform, analyzing 147 incidents across 81 runs. It shows that the standard reliability primitives—retry, timeout, and error‑rate circuit breaking—fail in practice, leading to costly loops, false trips, and blocked work. The authors identify two cross‑cutting causes—identity adequacy and evidence adequacy—and propose seven new reliability primitives that enforce reliability at the delegation level.
The paper investigates how AI agents that run long workflows using external tools can experience inconsistencies when retries, speculative execution, concurrency, or partial failures occur. It introduces an effect‑history model that distinguishes between actual external events and the agent’s observations, and catalogs eight common external‑effect anomalies. The authors analyze the standard Model Context Protocol tool interface, finding that its annotations are too coarse to fully express the necessary capabilities to prevent these anomalies, thereby motivating the need for reusable transactional contracts at the agent‑tool boundary.
arXiv:2608.30091v1 Announce Type: new Abstract: Modern agent frameworks compose planners, tool agents, remote services, and shared specialists into runtime delegation graphs, but their revocation API...
arXiv:2607. 11098v1 Announce Type: cross Abstract: Tool-using LLM agents are mostly evaluated assuming all tools work.
The paper introduces “Revise”, a runtime system that performs validity-guided, fine-grained recovery for online revisions in structured agent workflows. When a revision arrives, Revise intersects the change with recorded data and control dependencies, propagates the impact through the partially executed DAG, stops invalid work, preserves unaffected progress, and recomputes only the affected region. Experiments on real coding‑agent traces and LangGraph/LLMCompiler applications show that Revise matches a latest‑version oracle, reduces model calls by up to 56%, and improves service‑level objective goodput under load.
AcquireBound is a runtime authorization framework that ensures AI agents can safely acquire and activate resources such as compute, credentials, and services. It quarantines acquired outputs, resolves their capabilities through authenticated evidence, and activates them only after verifying a manifest, provenance, and relational constraints. The system demonstrates strong safety properties, passing extensive benign and unsafe trace tests across multiple resource classes.